Análisis PPM-S
library(sjPlot)
library(dplyr)
library(lavaan)
data01 <- sjlabelled::read_spss(path = "data/dat1.sav",verbose = FALSE)
dat01 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv01")) %>% na.omit()
dat02 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv02")) %>% na.omit()
dat03 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv03_p")) %>% na.omit()
Version 01:
- Percepcion esfuerzo
- Percepcion talento
- Percepcion familia rica
- Percepcion redes
- Preferencia esfuerzo
- Preferencia talento
- Preferencia familia rica
- Preferencia redes
model01 <- 'perc_merit=~meritv01_perc_effort+meritv01_perc_talent
perc_nmerit=~meritv01_perc_wpart+meritv01_perc_netw
pref_merit=~meritv01_pref_effort+meritv01_pref_talent
pref_nmerit=~meritv01_pref_wpart+meritv01_pref_netw'
fit1 <- cfa(model = model01,data = dat01,ordered = c("meritv01_perc_effort","meritv01_perc_talent",
"meritv01_perc_wpart","meritv01_perc_netw",
"meritv01_pref_effort","meritv01_pref_talent",
"meritv01_pref_wpart","meritv01_pref_netw"))
summary(fit1,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit1,what = "std",thresholds = FALSE, intercepts = FALSE)
## lavaan 0.6-4 ended normally after 41 iterations
##
## Optimization method NLMINB
## Number of free parameters 46
##
## Number of observations 410
##
## Estimator DWLS Robust
## Model Fit Test Statistic 25.944 45.923
## Degrees of freedom 14 14
## P-value (Chi-square) 0.026 0.000
## Scaling correction factor 0.602
## Shift parameter 2.827
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 3417.782 2335.438
## Degrees of freedom 28 28
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.996 0.986
## Tucker-Lewis Index (TLI) 0.993 0.972
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.046 0.075
## 90 Percent Confidence Interval 0.015 0.073 0.051 0.099
## P-value RMSEA <= 0.05 0.565 0.042
##
## Robust RMSEA NA
## 90 Percent Confidence Interval NA NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.043 0.043
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Unstructured
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit =~
## mrtv01_prc_ffr 1.000 0.681 0.681
## mrtv01_prc_tln 1.258 0.148 8.529 0.000 0.857 0.857
## perc_nmerit =~
## mrtv01_prc_wpr 1.000 0.810 0.810
## mrtv01_prc_ntw 1.216 0.099 12.329 0.000 0.985 0.985
## pref_merit =~
## mrtv01_prf_ffr 1.000 0.816 0.816
## mrtv01_prf_tln 0.779 0.078 10.009 0.000 0.635 0.635
## pref_nmerit =~
## mrtv01_prf_wpr 1.000 0.735 0.735
## mrtv01_prf_ntw 1.255 0.299 4.195 0.000 0.922 0.922
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit ~~
## perc_nmerit -0.033 0.033 -1.022 0.307 -0.060 -0.060
## pref_merit 0.284 0.043 6.630 0.000 0.510 0.510
## pref_nmerit 0.120 0.039 3.101 0.002 0.239 0.239
## perc_nmerit ~~
## pref_merit 0.330 0.044 7.432 0.000 0.499 0.499
## pref_nmerit -0.008 0.035 -0.217 0.828 -0.013 -0.013
## pref_merit ~~
## pref_nmerit 0.108 0.043 2.509 0.012 0.180 0.180
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mrtv01_prc_ffr 0.000 0.000 0.000
## .mrtv01_prc_tln 0.000 0.000 0.000
## .mrtv01_prc_wpr 0.000 0.000 0.000
## .mrtv01_prc_ntw 0.000 0.000 0.000
## .mrtv01_prf_ffr 0.000 0.000 0.000
## .mrtv01_prf_tln 0.000 0.000 0.000
## .mrtv01_prf_wpr 0.000 0.000 0.000
## .mrtv01_prf_ntw 0.000 0.000 0.000
## perc_merit 0.000 0.000 0.000
## perc_nmerit 0.000 0.000 0.000
## pref_merit 0.000 0.000 0.000
## pref_nmerit 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mrtv01_prc_f|1 -1.165 0.080 -14.571 0.000 -1.165 -1.165
## mrtv01_prc_f|2 -0.455 0.064 -7.076 0.000 -0.455 -0.455
## mrtv01_prc_f|3 0.031 0.062 0.493 0.622 0.031 0.031
## mrtv01_prc_f|4 0.741 0.069 10.809 0.000 0.741 0.741
## mrtv01_prc_t|1 -1.254 0.083 -15.041 0.000 -1.254 -1.254
## mrtv01_prc_t|2 -0.408 0.064 -6.394 0.000 -0.408 -0.408
## mrtv01_prc_t|3 0.389 0.064 6.100 0.000 0.389 0.389
## mrtv01_prc_t|4 1.268 0.084 15.102 0.000 1.268 1.268
## mrtv01_prc_w|1 -1.296 0.085 -15.220 0.000 -1.296 -1.296
## mrtv01_prc_w|2 -0.868 0.071 -12.183 0.000 -0.868 -0.868
## mrtv01_prc_w|3 -0.253 0.063 -4.041 0.000 -0.253 -0.253
## mrtv01_prc_w|4 0.356 0.063 5.611 0.000 0.356 0.356
## mrtv01_prc_n|1 -1.435 0.092 -15.638 0.000 -1.435 -1.435
## mrtv01_prc_n|2 -1.042 0.076 -13.727 0.000 -1.042 -1.042
## mrtv01_prc_n|3 -0.560 0.066 -8.529 0.000 -0.560 -0.560
## mrtv01_prc_n|4 0.469 0.065 7.271 0.000 0.469 0.469
## mrtv01_prf_f|1 -1.547 0.098 -15.769 0.000 -1.547 -1.547
## mrtv01_prf_f|2 -1.165 0.080 -14.571 0.000 -1.165 -1.165
## mrtv01_prf_f|3 -0.663 0.067 -9.868 0.000 -0.663 -0.663
## mrtv01_prf_f|4 0.166 0.062 2.662 0.008 0.166 0.166
## mrtv01_prf_t|1 -1.489 0.095 -15.723 0.000 -1.489 -1.489
## mrtv01_prf_t|2 -0.790 0.070 -11.365 0.000 -0.790 -0.790
## mrtv01_prf_t|3 0.141 0.062 2.268 0.023 0.141 0.141
## mrtv01_prf_t|4 0.914 0.072 12.626 0.000 0.914 0.914
## mrtv01_prf_w|1 -0.807 0.070 -11.549 0.000 -0.807 -0.807
## mrtv01_prf_w|2 -0.135 0.062 -2.170 0.030 -0.135 -0.135
## mrtv01_prf_w|3 0.816 0.070 11.640 0.000 0.816 0.816
## mrtv01_prf_w|4 1.734 0.111 15.606 0.000 1.734 1.734
## mrtv01_prf_n|1 -0.663 0.067 -9.868 0.000 -0.663 -0.663
## mrtv01_prf_n|2 0.086 0.062 1.381 0.167 0.086 0.086
## mrtv01_prf_n|3 1.052 0.076 13.808 0.000 1.052 1.052
## mrtv01_prf_n|4 1.971 0.133 14.789 0.000 1.971 1.971
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mrtv01_prc_ffr 0.536 0.536 0.536
## .mrtv01_prc_tln 0.265 0.265 0.265
## .mrtv01_prc_wpr 0.344 0.344 0.344
## .mrtv01_prc_ntw 0.030 0.030 0.030
## .mrtv01_prf_ffr 0.335 0.335 0.335
## .mrtv01_prf_tln 0.596 0.596 0.596
## .mrtv01_prf_wpr 0.460 0.460 0.460
## .mrtv01_prf_ntw 0.150 0.150 0.150
## perc_merit 0.464 0.064 7.201 0.000 1.000 1.000
## perc_nmerit 0.656 0.059 11.060 0.000 1.000 1.000
## pref_merit 0.665 0.072 9.221 0.000 1.000 1.000
## pref_nmerit 0.540 0.131 4.127 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mrtv01_prc_ffr 1.000 1.000 1.000
## mrtv01_prc_tln 1.000 1.000 1.000
## mrtv01_prc_wpr 1.000 1.000 1.000
## mrtv01_prc_ntw 1.000 1.000 1.000
## mrtv01_prf_ffr 1.000 1.000 1.000
## mrtv01_prf_tln 1.000 1.000 1.000
## mrtv01_prf_wpr 1.000 1.000 1.000
## mrtv01_prf_ntw 1.000 1.000 1.000
Version 02:
- Percepcion esfuerzo
- Preferencia esfuerzo
- Percepcion talento
- Preferencia talento
- Percepcion familia rica
- Preferencia familia rica
- Percepcion redes
- Preferencia redes
model02 <- 'perc_merit=~meritv02_perc_effort+meritv02_perc_talent
perc_nmerit=~meritv02_perc_wpart+meritv02_perc_netw
pref_merit=~meritv02_pref_effort+meritv02_pref_talent
pref_nmerit=~meritv02_pref_wpart+meritv02_pref_netw'
fit2 <- cfa(model = model02,data = dat02,ordered = c("meritv02_perc_effort","meritv02_perc_talent",
"meritv02_perc_wpart","meritv02_perc_netw",
"meritv02_pref_effort","meritv02_pref_talent",
"meritv02_pref_wpart","meritv02_pref_netw"))
## Warning in lav_object_post_check(object): lavaan WARNING: some estimated ov
## variances are negative
summary(fit2,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit2,what = "std")
## lavaan 0.6-4 ended normally after 37 iterations
##
## Optimization method NLMINB
## Number of free parameters 46
##
## Number of observations 412
##
## Estimator DWLS Robust
## Model Fit Test Statistic 49.320 69.598
## Degrees of freedom 14 14
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 0.728
## Shift parameter 1.866
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 1565.199 1156.514
## Degrees of freedom 28 28
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.977 0.951
## Tucker-Lewis Index (TLI) 0.954 0.901
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.078 0.098
## 90 Percent Confidence Interval 0.055 0.103 0.076 0.122
## P-value RMSEA <= 0.05 0.023 0.000
##
## Robust RMSEA NA
## 90 Percent Confidence Interval NA NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.057 0.057
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Unstructured
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit =~
## mrtv02_prc_ffr 1.000 0.809 0.809
## mrtv02_prc_tln 0.815 0.075 10.828 0.000 0.660 0.660
## perc_nmerit =~
## mrtv02_prc_wpr 1.000 0.790 0.790
## mrtv02_prc_ntw 1.020 0.115 8.851 0.000 0.806 0.806
## pref_merit =~
## mrtv02_prf_ffr 1.000 0.828 0.828
## mrtv02_prf_tln 0.724 0.065 11.056 0.000 0.599 0.599
## pref_nmerit =~
## mrtv02_prf_wpr 1.000 1.285 1.285
## mrtv02_prf_ntw 0.286 0.197 1.457 0.145 0.368 0.368
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit ~~
## perc_nmerit 0.007 0.041 0.162 0.871 0.010 0.010
## pref_merit 0.457 0.039 11.794 0.000 0.682 0.682
## pref_nmerit 0.180 0.050 3.561 0.000 0.173 0.173
## perc_nmerit ~~
## pref_merit 0.335 0.045 7.371 0.000 0.512 0.512
## pref_nmerit 0.164 0.045 3.649 0.000 0.161 0.161
## pref_merit ~~
## pref_nmerit 0.081 0.049 1.657 0.097 0.077 0.077
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mrtv02_prc_ffr 0.000 0.000 0.000
## .mrtv02_prc_tln 0.000 0.000 0.000
## .mrtv02_prc_wpr 0.000 0.000 0.000
## .mrtv02_prc_ntw 0.000 0.000 0.000
## .mrtv02_prf_ffr 0.000 0.000 0.000
## .mrtv02_prf_tln 0.000 0.000 0.000
## .mrtv02_prf_wpr 0.000 0.000 0.000
## .mrtv02_prf_ntw 0.000 0.000 0.000
## perc_merit 0.000 0.000 0.000
## perc_nmerit 0.000 0.000 0.000
## pref_merit 0.000 0.000 0.000
## pref_nmerit 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mrtv02_prc_f|1 -0.917 0.072 -12.689 0.000 -0.917 -0.917
## mrtv02_prc_f|2 -0.315 0.063 -5.011 0.000 -0.315 -0.315
## mrtv02_prc_f|3 0.073 0.062 1.181 0.238 0.073 0.073
## mrtv02_prc_f|4 0.761 0.069 11.064 0.000 0.761 0.761
## mrtv02_prc_t|1 -1.298 0.085 -15.268 0.000 -1.298 -1.298
## mrtv02_prc_t|2 -0.487 0.065 -7.544 0.000 -0.487 -0.487
## mrtv02_prc_t|3 0.233 0.062 3.736 0.000 0.233 0.233
## mrtv02_prc_t|4 1.327 0.086 15.377 0.000 1.327 1.327
## mrtv02_prc_w|1 -1.218 0.082 -14.899 0.000 -1.218 -1.218
## mrtv02_prc_w|2 -0.828 0.070 -11.797 0.000 -0.828 -0.828
## mrtv02_prc_w|3 -0.387 0.064 -6.086 0.000 -0.387 -0.387
## mrtv02_prc_w|4 0.348 0.063 5.500 0.000 0.348 0.348
## mrtv02_prc_n|1 -1.455 0.093 -15.713 0.000 -1.455 -1.455
## mrtv02_prc_n|2 -0.964 0.073 -13.120 0.000 -0.964 -0.964
## mrtv02_prc_n|3 -0.460 0.064 -7.156 0.000 -0.460 -0.460
## mrtv02_prc_n|4 0.593 0.066 8.989 0.000 0.593 0.593
## mrtv02_prf_f|1 -1.156 0.079 -14.552 0.000 -1.156 -1.156
## mrtv02_prf_f|2 -0.964 0.073 -13.120 0.000 -0.964 -0.964
## mrtv02_prf_f|3 -0.593 0.066 -8.989 0.000 -0.593 -0.593
## mrtv02_prf_f|4 0.303 0.063 4.815 0.000 0.303 0.303
## mrtv02_prf_t|1 -1.591 0.101 -15.812 0.000 -1.591 -1.591
## mrtv02_prf_t|2 -0.721 0.068 -10.599 0.000 -0.721 -0.721
## mrtv02_prf_t|3 0.196 0.062 3.147 0.002 0.196 0.196
## mrtv02_prf_t|4 1.110 0.078 14.256 0.000 1.110 1.110
## mrtv02_prf_w|1 -0.854 0.071 -12.068 0.000 -0.854 -0.854
## mrtv02_prf_w|2 -0.328 0.063 -5.207 0.000 -0.328 -0.328
## mrtv02_prf_w|3 0.557 0.065 8.509 0.000 0.557 0.557
## mrtv02_prf_w|4 1.591 0.101 15.812 0.000 1.591 1.591
## mrtv02_prf_n|1 -0.721 0.068 -10.599 0.000 -0.721 -0.721
## mrtv02_prf_n|2 0.097 0.062 1.574 0.115 0.097 0.097
## mrtv02_prf_n|3 1.004 0.075 13.457 0.000 1.004 1.004
## mrtv02_prf_n|4 1.858 0.122 15.291 0.000 1.858 1.858
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mrtv02_prc_ffr 0.345 0.345 0.345
## .mrtv02_prc_tln 0.565 0.565 0.565
## .mrtv02_prc_wpr 0.376 0.376 0.376
## .mrtv02_prc_ntw 0.351 0.351 0.351
## .mrtv02_prf_ffr 0.314 0.314 0.314
## .mrtv02_prf_tln 0.641 0.641 0.641
## .mrtv02_prf_wpr -0.651 -0.651 -0.651
## .mrtv02_prf_ntw 0.865 0.865 0.865
## perc_merit 0.655 0.070 9.336 0.000 1.000 1.000
## perc_nmerit 0.624 0.079 7.917 0.000 1.000 1.000
## pref_merit 0.686 0.072 9.561 0.000 1.000 1.000
## pref_nmerit 1.651 1.116 1.479 0.139 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mrtv02_prc_ffr 1.000 1.000 1.000
## mrtv02_prc_tln 1.000 1.000 1.000
## mrtv02_prc_wpr 1.000 1.000 1.000
## mrtv02_prc_ntw 1.000 1.000 1.000
## mrtv02_prf_ffr 1.000 1.000 1.000
## mrtv02_prf_tln 1.000 1.000 1.000
## mrtv02_prf_wpr 1.000 1.000 1.000
## mrtv02_prf_ntw 1.000 1.000 1.000
Version 03: orden aleatorio
model03 <- 'perc_merit=~meritv03_perc_effort+meritv03_perc_talent
perc_nmerit=~meritv03_perc_wpart+meritv03_perc_netw
pref_merit=~meritv03_pref_effort+meritv03_pref_talent
pref_nmerit=~meritv03_pref_wpart+meritv03_pref_netw'
fit3 <- cfa(model = model03,data = dat03,ordered = c("meritv03_perc_effort","meritv03_perc_talent",
"meritv03_perc_wpart","meritv03_perc_netw",
"meritv03_pref_effort","meritv03_pref_talent",
"meritv03_pref_wpart","meritv03_pref_netw"))
summary(fit3,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit3,what = "std",thresholds = FALSE, intercepts = FALSE)
## lavaan 0.6-4 ended normally after 39 iterations
##
## Optimization method NLMINB
## Number of free parameters 46
##
## Number of observations 410
##
## Estimator DWLS Robust
## Model Fit Test Statistic 25.502 39.240
## Degrees of freedom 14 14
## P-value (Chi-square) 0.030 0.000
## Scaling correction factor 0.692
## Shift parameter 2.374
## for simple second-order correction (Mplus variant)
##
## Model test baseline model:
##
## Minimum Function Test Statistic 1746.673 1316.959
## Degrees of freedom 28 28
## P-value 0.000 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.993 0.980
## Tucker-Lewis Index (TLI) 0.987 0.961
##
## Robust Comparative Fit Index (CFI) NA
## Robust Tucker-Lewis Index (TLI) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.045 0.066
## 90 Percent Confidence Interval 0.014 0.072 0.042 0.091
## P-value RMSEA <= 0.05 0.585 0.123
##
## Robust RMSEA NA
## 90 Percent Confidence Interval NA NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.043 0.043
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Unstructured
## Standard Errors Robust.sem
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit =~
## mrtv03_prc_ffr 1.000 0.655 0.655
## mrtv03_prc_tln 1.060 0.189 5.610 0.000 0.694 0.694
## perc_nmerit =~
## mrtv03_prc_wpr 1.000 0.793 0.793
## mrtv03_prc_ntw 1.145 0.133 8.637 0.000 0.908 0.908
## pref_merit =~
## mrtv03_prf_ffr 1.000 0.731 0.731
## mrtv03_prf_tln 0.847 0.110 7.719 0.000 0.619 0.619
## pref_nmerit =~
## mrtv03_prf_wpr 1.000 0.602 0.602
## mrtv03_prf_ntw 1.575 0.340 4.632 0.000 0.949 0.949
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit ~~
## perc_nmerit -0.032 0.035 -0.899 0.369 -0.061 -0.061
## pref_merit 0.205 0.042 4.930 0.000 0.429 0.429
## pref_nmerit 0.154 0.041 3.753 0.000 0.389 0.389
## perc_nmerit ~~
## pref_merit 0.327 0.046 7.125 0.000 0.564 0.564
## pref_nmerit -0.060 0.031 -1.963 0.050 -0.126 -0.126
## pref_merit ~~
## pref_nmerit 0.044 0.034 1.290 0.197 0.099 0.099
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mrtv03_prc_ffr 0.000 0.000 0.000
## .mrtv03_prc_tln 0.000 0.000 0.000
## .mrtv03_prc_wpr 0.000 0.000 0.000
## .mrtv03_prc_ntw 0.000 0.000 0.000
## .mrtv03_prf_ffr 0.000 0.000 0.000
## .mrtv03_prf_tln 0.000 0.000 0.000
## .mrtv03_prf_wpr 0.000 0.000 0.000
## .mrtv03_prf_ntw 0.000 0.000 0.000
## perc_merit 0.000 0.000 0.000
## perc_nmerit 0.000 0.000 0.000
## pref_merit 0.000 0.000 0.000
## pref_nmerit 0.000 0.000 0.000
##
## Thresholds:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mrtv03_prc_f|1 -1.228 0.082 -14.913 0.000 -1.228 -1.228
## mrtv03_prc_f|2 -0.497 0.065 -7.659 0.000 -0.497 -0.497
## mrtv03_prc_f|3 -0.160 0.062 -2.564 0.010 -0.160 -0.160
## mrtv03_prc_f|4 0.546 0.065 8.336 0.000 0.546 0.546
## mrtv03_prc_t|1 -1.282 0.085 -15.162 0.000 -1.282 -1.282
## mrtv03_prc_t|2 -0.442 0.064 -6.881 0.000 -0.442 -0.442
## mrtv03_prc_t|3 0.141 0.062 2.268 0.023 0.141 0.141
## mrtv03_prc_t|4 0.932 0.073 12.801 0.000 0.932 0.932
## mrtv03_prc_w|1 -1.268 0.084 -15.102 0.000 -1.268 -1.268
## mrtv03_prc_w|2 -0.757 0.069 -10.996 0.000 -0.757 -0.757
## mrtv03_prc_w|3 -0.402 0.064 -6.296 0.000 -0.402 -0.402
## mrtv03_prc_w|4 0.298 0.063 4.728 0.000 0.298 0.298
## mrtv03_prc_n|1 -1.453 0.093 -15.670 0.000 -1.453 -1.453
## mrtv03_prc_n|2 -1.031 0.076 -13.646 0.000 -1.031 -1.031
## mrtv03_prc_n|3 -0.596 0.066 -9.010 0.000 -0.596 -0.596
## mrtv03_prc_n|4 0.203 0.062 3.253 0.001 0.203 0.203
## mrtv03_prf_f|1 -1.489 0.095 -15.723 0.000 -1.489 -1.489
## mrtv03_prf_f|2 -1.268 0.084 -15.102 0.000 -1.268 -1.268
## mrtv03_prf_f|3 -0.807 0.070 -11.549 0.000 -0.807 -0.807
## mrtv03_prf_f|4 0.012 0.062 0.197 0.844 0.012 0.012
## mrtv03_prf_t|1 -1.402 0.090 -15.564 0.000 -1.402 -1.402
## mrtv03_prf_t|2 -0.749 0.069 -10.903 0.000 -0.749 -0.749
## mrtv03_prf_t|3 -0.031 0.062 -0.493 0.622 -0.031 -0.031
## mrtv03_prf_t|4 0.663 0.067 9.868 0.000 0.663 0.663
## mrtv03_prf_w|1 -0.895 0.072 -12.450 0.000 -0.895 -0.895
## mrtv03_prf_w|2 -0.247 0.063 -3.942 0.000 -0.247 -0.247
## mrtv03_prf_w|3 0.449 0.064 6.979 0.000 0.449 0.449
## mrtv03_prf_w|4 1.310 0.086 15.275 0.000 1.310 1.310
## mrtv03_prf_n|1 -0.757 0.069 -10.996 0.000 -0.757 -0.757
## mrtv03_prf_n|2 0.055 0.062 0.888 0.375 0.055 0.055
## mrtv03_prf_n|3 0.694 0.068 10.247 0.000 0.694 0.694
## mrtv03_prf_n|4 1.567 0.099 15.774 0.000 1.567 1.567
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .mrtv03_prc_ffr 0.571 0.571 0.571
## .mrtv03_prc_tln 0.518 0.518 0.518
## .mrtv03_prc_wpr 0.371 0.371 0.371
## .mrtv03_prc_ntw 0.175 0.175 0.175
## .mrtv03_prf_ffr 0.465 0.465 0.465
## .mrtv03_prf_tln 0.616 0.616 0.616
## .mrtv03_prf_wpr 0.637 0.637 0.637
## .mrtv03_prf_ntw 0.100 0.100 0.100
## perc_merit 0.429 0.084 5.135 0.000 1.000 1.000
## perc_nmerit 0.629 0.079 7.939 0.000 1.000 1.000
## pref_merit 0.535 0.082 6.548 0.000 1.000 1.000
## pref_nmerit 0.363 0.085 4.290 0.000 1.000 1.000
##
## Scales y*:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## mrtv03_prc_ffr 1.000 1.000 1.000
## mrtv03_prc_tln 1.000 1.000 1.000
## mrtv03_prc_wpr 1.000 1.000 1.000
## mrtv03_prc_ntw 1.000 1.000 1.000
## mrtv03_prf_ffr 1.000 1.000 1.000
## mrtv03_prf_tln 1.000 1.000 1.000
## mrtv03_prf_wpr 1.000 1.000 1.000
## mrtv03_prf_ntw 1.000 1.000 1.000
Version 04: muestra completa
dat04 <- data01 %>% filter(Intro==1) %>% select(starts_with("meritv01"),starts_with("meritv02"),starts_with("meritv03_p"))
dat04$perc_effort <- rowSums(dat04[,c(matches(match = "perc_effort",vars = names(dat04)))],na.rm = TRUE)
dat04$perc_talent <- rowSums(dat04[,c(matches(match = "perc_talent",vars = names(dat04)))],na.rm = TRUE)
dat04$perc_wpart <- rowSums(dat04[,c(matches(match = "perc_wpart" ,vars = names(dat04)))],na.rm = TRUE)
dat04$perc_netw <- rowSums(dat04[,c(matches(match = "perc_netw" ,vars = names(dat04)))],na.rm = TRUE)
dat04$pref_effort <- rowSums(dat04[,c(matches(match = "pref_effort",vars = names(dat04)))],na.rm = TRUE)
dat04$pref_talent <- rowSums(dat04[,c(matches(match = "pref_talent",vars = names(dat04)))],na.rm = TRUE)
dat04$pref_wpart <- rowSums(dat04[,c(matches(match = "pref_wpart" ,vars = names(dat04)))],na.rm = TRUE)
dat04$pref_netw <- rowSums(dat04[,c(matches(match = "pref_netw" ,vars = names(dat04)))],na.rm = TRUE)
model04 <- 'perc_merit=~perc_effort+perc_talent
perc_nmerit=~perc_wpart+perc_netw
pref_merit=~pref_effort+pref_talent
pref_nmerit=~pref_wpart+pref_netw'
fit4 <- cfa(model = model04,data = dat04)
summary(fit4,standardized=TRUE, fit.measures=TRUE)
semPlot::semPaths(object = fit4,what = "std",thresholds = FALSE, intercepts = FALSE)
## lavaan 0.6-4 ended normally after 46 iterations
##
## Optimization method NLMINB
## Number of free parameters 22
##
## Number of observations 1234
##
## Estimator ML
## Model Fit Test Statistic 152.731
## Degrees of freedom 14
## P-value (Chi-square) 0.000
##
## Model test baseline model:
##
## Minimum Function Test Statistic 2175.728
## Degrees of freedom 28
## P-value 0.000
##
## User model versus baseline model:
##
## Comparative Fit Index (CFI) 0.935
## Tucker-Lewis Index (TLI) 0.871
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -14994.788
## Loglikelihood unrestricted model (H1) -14918.422
##
## Number of free parameters 22
## Akaike (AIC) 30033.576
## Bayesian (BIC) 30146.172
## Sample-size adjusted Bayesian (BIC) 30076.290
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.090
## 90 Percent Confidence Interval 0.077 0.103
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.044
##
## Parameter Estimates:
##
## Information Expected
## Information saturated (h1) model Structured
## Standard Errors Standard
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit =~
## perc_effort 1.000 1.034 0.744
## perc_talent 0.721 0.061 11.840 0.000 0.745 0.629
## perc_nmerit =~
## perc_wpart 1.000 0.976 0.727
## perc_netw 1.081 0.071 15.129 0.000 1.055 0.874
## pref_merit =~
## pref_effort 1.000 0.955 0.774
## pref_talent 0.658 0.051 12.999 0.000 0.629 0.542
## pref_nmerit =~
## pref_wpart 1.000 0.737 0.622
## pref_netw 1.263 0.210 6.018 0.000 0.931 0.833
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## perc_merit ~~
## perc_nmerit -0.001 0.039 -0.030 0.976 -0.001 -0.001
## pref_merit 0.515 0.048 10.621 0.000 0.522 0.522
## pref_nmerit 0.229 0.044 5.196 0.000 0.300 0.300
## perc_nmerit ~~
## pref_merit 0.509 0.047 10.798 0.000 0.546 0.546
## pref_nmerit 0.012 0.026 0.459 0.646 0.017 0.017
## pref_merit ~~
## pref_nmerit 0.066 0.030 2.204 0.028 0.094 0.094
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .perc_effort 0.860 0.091 9.428 0.000 0.860 0.446
## .perc_talent 0.849 0.056 15.280 0.000 0.849 0.605
## .perc_wpart 0.848 0.067 12.719 0.000 0.848 0.471
## .perc_netw 0.345 0.068 5.044 0.000 0.345 0.237
## .pref_effort 0.608 0.067 9.038 0.000 0.608 0.400
## .pref_talent 0.952 0.047 20.267 0.000 0.952 0.707
## .pref_wpart 0.861 0.095 9.053 0.000 0.861 0.613
## .pref_netw 0.382 0.142 2.691 0.007 0.382 0.306
## perc_merit 1.068 0.109 9.773 0.000 1.000 1.000
## perc_nmerit 0.952 0.086 11.092 0.000 1.000 1.000
## pref_merit 0.912 0.084 10.840 0.000 1.000 1.000
## pref_nmerit 0.544 0.099 5.481 0.000 1.000 1.000